Population size affected by environmental variability impacts genetics, traits, and plant performance in Trifolium montanum L.

Abstract Population size, genetic diversity, and performance have fundamental importance for ecology, evolution, and nature conservation of plant species. Despite well‐studied relationships among environmental, genetic, and intraspecific trait variation (ITV), the influence of population size on these aspects is less understood. To assess the sources of population size variation, but also its impact on genetic, functional trait, and performance aspects, we conducted detailed population size estimations, assessed 23 abiotic and biotic environmental habitat factors, performed population genetic analyses using nine microsatellite markers, and recorded nine functional traits based on 260 Trifolium montanum individuals from 13 semi‐dry grassland locations of Central Europe. Modern statistical analyses based on a multivariate framework (path analysis) with preselected linear regression models revealed that the variation of abiotic factors (in contrast to factors per se) almost completely, significantly explained fluctuations in population size (R 2 = .93). In general, abiotic habitat variation (heterogeneity) was not affected by habitat area. Population size significantly explained genetic diversity (N A: R 2 = .42, H o: R 2 = .67, H e: R 2 = .43, and I: R 2 = .59), inbreeding (F IS: R 2 = .35), and differentiation (G ST: R 2 = .20). We also found that iFDCV (ITV) was significantly explained by abiotic habitat heterogeneity, and to a lesser extent by genetic diversity H e (R 2 = .81). Nevertheless, habitat heterogeneity did not statistically affect genetic diversity. This may be due to the use of selectively neutral microsatellite markers, and possibly by insufficient abiotic selective pressures on habitats examined. Small T. montanum populations in nonoptimal habitats were characterized by reduced genetic and functional trait diversity, and elevated genetic inbreeding and differentiation. This indicates reduced adaptability to current and future environmental changes. The long‐term survival of small populations with reduced genetic diversity and beginning inbreeding will be highly dependent on habitat protection and adequate land‐use actions.


| INTRODUC TI ON
Relationships among population size, genetic diversity, and performance are of great research interest due to their fundamental importance in plant ecology, evolution, and conservation (De Kort et al., 2021;Leimu et al., 2006;Rosche et al., 2022;Szczecińska et al., 2016). Genetic diversity is generated by DNA mutation and/ or meiotic recombination. It provides variation for natural selection to act on, and is, therefore, crucial for evolutionary processes and influences the adaptive potential of species to current and future environmental conditions (Boulding, 2008;Karbstein et al., 2019;Karbstein, Prinz, et al., 2020;Reed & Frankham, 2003). Population size is positively linked to genetic diversity of populations: the larger the population size, the higher the probability of having genetically diverse individuals generated by mutation and recombination, and the lower the consequences of genetic drift. The latter refers to random genetic changes in allele frequencies within populations, which occur, for example, when population size is reduced by habitat destruction or degradation (bottleneck), or dispersal of a few individuals to remote locations (founder effect; reviewed in Freeland et al., 2011).
In small populations, reduced genetic diversity and its negative consequences are frequently observed. They are caused by loss of heterozygosity due to elevated genetic drift (incl. founder and bottleneck effects), and inbreeding depression due to the accumulation of deleterious mutations (Caré et al., 2020;Freeland et al., 2011;Karbstein, Rahmsdorf, et al., 2020;Lynch et al., 1995;Rosche et al., 2022;Schleuning et al., 2009). This leads to reduced performance (i.e., plant function, health, or survival) and fitness (i.e., reproductive output) in small populations, and in the long term, to reduced evolutionary potential to adapt to changing environments and increased risk of extinction (Ellstrand & Elam, 1993;Karbstein, Rahmsdorf, et al., 2020;Leimu et al., 2006;Spielman et al., 2004).
In nature, many plant populations are isolated and small, and recent anthropogenic habitat fragmentation further increases isolation and promotes erosion of these populations. However, within plant species, the precise consequences of changes in population size for genetic diversity and inbreeding, as well as performance and fitness of populations, are less understood.
Second, variation in habitat quality, age, and structure across the species' distribution range can also affect these relationships. For instance, suitable habitat quality (niche optimum) typically leads to large populations with high growth rates, whereas poor habitat quality (niche pessimum) leads to small populations with low growth rates, resulting in changes in genetic diversity, performance, and fitness (Leimu et al., 2006;Karbstein, Prinz, et al., 2020;Reisch et al., 2021).
These thoughts are also apprehended and summarized by the "abundant center hypothesis" (ACH; Sagarin et al., 2006;Sagarin & Gaines, 2002). The ACH predicts the largest population size along with the highest genetic diversity, performance, and fitness for niche-optimum (range center) populations, but a decline towards niche-pessimum (range edge) populations due to decreasing habitat quality, and effects of genetic drift, restricted gene flow, and inbreeding as well as increasing genetic differentiation (Brown, 1984;Hampe & Petit, 2005;Hardie & Hutchings, 2010;Hirsch et al., 2015;Hoffmann & Blows, 1994;Wagner et al., 2012). However, highly genetically differentiated, niche-pessimum/marginal range populations may still have sufficient genetic variation and can be valuable sources and important targets for nature conservation efforts due to site-specific adaptations (Karbstein, Prinz, et al., 2020;Kirschner et al., 2020). General relationships also depend on several other factors, such as plant breeding system, life history, and species rarity, but also on the use of neutral or selective genetic markers (reviewed in Angeloni et al., 2011;Hamrick et al., 1979;Reed & Frankham, 2003;Reisch & Bernhardt-Römermann, 2014;Spielman et al., 2004). Nevertheless, detailed intraspecific population size measurements affected by local, comprehensive abiotic and biotic records based on sufficient replications are often missing for single plant species, attributable to high sampling efforts and a lack of suitable model systems. Consequently, the intraspecific link between population size, genetics and fitness to the local environment is not sufficiently understood.

T A X O N O M Y C L A S S I F I C A T I O N
Applied ecology, Biodiversity ecology, Botany, Conservation ecology, Conservation genetics, Ecological genetics, Functional ecology, Genetics, Population ecology, Spatial ecology releasing height "RH," or leaf area "LA"), while others capture plant physiology and function (e.g., specific leaf area "SLA," or performance index "PI"; see also Díaz et al., 2016). Functional traits are often used to explain individual but also population and ecosystem responses related to environmental conditions and changes such as habitat fragmentation or climate change (Bernhardt-Römermann et al., 2011;Bucher et al., 2016;Karbstein et al., 2019;Römermann et al., 2009;Westerband et al., 2021). They are strongly dependent on local abiotic soil and climatic factors and biotic competition, are highly species-specific, and thus should be studied for each model system.
Variation of functional traits is initially measured as phenotypic plasticity, that is, the ability of a single genotype to express different phenotypes depending on its abiotic and biotic environment (Gratani, 2014;Sultan, 2000). Phenotypic plasticity has an (epi)genetic basis and contributes to genetic differentiation and speciation processes (Agrawal, 2001;Westerband et al., 2021). Genotypedependent plasticity of individuals results in phenotypic variation of a given plant population. Observations in natural plant populations have shown that phenotypic and genetic variation is associated with each other, particularly with respect to morphology-related (Karbstein, Prinz, et al., 2020;Waitt & Levin, 1998) but also ecological or ecophysiologically important traits (Ackerly et al., 2000;Hughes et al., 2019;Locascio et al., 2009;Via et al., 1995). In the semi-dry grassland species T. montanum (mountain clover), intraspecific trait variation (ITV) based on functional traits was significantly positively associated with environmental habitat heterogeneity and genetic diversity of populations (Karbstein, Prinz, et al., 2020). Though abiotic habitat heterogeneity was predominantly responsible for ITV, both aspects are important to consider when studying the consequences for plant performance under present and changing environmental conditions (Karbstein et al., 2019;Karbstein, Prinz, et al., 2020).
Despite the aforementioned study inferred positive mean relationships, the influence of population size on these relationships remains unobserved to date.
Trifolium montanum populations in (semi-)dry calcareous grasslands of Central Europe are well suited to fill the gaps of knowledge.
Formerly, T. montanum was widespread, but its abundance in Central Europe declined during the last decades due to habitat degradation and fragmentation, and today the species is regionally threatened (Breunig & Demuth, 1999;Garve, 2004;Matter et al., 2012).
Strategies for the protection and management of (semi-)dry grasslands and their endangered species continue to be a hot topic for both theorists and practitioners involved in conservation biology. Therefore, in this study, we aim to analyze relationships among population size, environment, genetic diversity and inbreeding, and population performance based on functional traits regarding the herbaceous, calcareous (semi-)dry grassland species T. montanum.
We addressed the following questions: Do small and large T. montanum populations differ in their (1) abiotic and biotic environments, in their (2) functional trait characteristics, and (3) genetic diversity, inbreeding, and differentiation? And (4) how population size affected by environmental factors impacts genetic features and intraspecific trait variation (ITV)? Results will subsequently be discussed in the context of long-term viability and nature conservation of T. montanum populations.
Seed dispersal starts in July and mainly occurs on a regional scale (Schleuning et al., 2009;Schleuning & Matthies, 2008). In the course of this study, we observed local grazing by sheep, goats, cattle, and horses, suggesting endozoochorous, geographically restricted seed dispersal in Central Germany. Vegetative reproduction has been frequently observed in T. montanum (Klimeš & Klimešová, 1999;Klimešová et al., 2017;Klimešová & Bello, 2009). In this study, 30% of the sampled T. montanum individuals showed clonality in the form of epigeogenous stems and root splitters. Clones are connected and/or are growing very close to mother plants, and were easily sorted out beforehand. We also found no evidence of sampled clones within populations across the dataset (see assessment of population genetics below). In addition, the main root varies remarkably in thickness and length ( Figure S1a,b). Among study locations, we observed a maximum diameter and length of approximately 20 mm and 20 cm, respectively, presumably due to age-related and/or environmental effects. For example, an up to 30-year-old individual was observed, and far older ones are expected ( Figure S1b).

| Study locations, estimation of population size, and sampling
We sampled 13 locations in Central Europe, covering a wide range of environmental conditions (Table 1, Figure 1, see also Karbstein, Prinz, et al., 2020). To estimate population size at each location, we carried out two different strategies. The number of individuals was counted if populations contained less than 500 individuals and rounded to the nearest 10, or extrapolated by averaging the number of individuals from 15 to 20, 4 m 2 -records (individuals per m 2 , abundance) multiplied by the area occupied by a population (recorded with GPX-tracks, Figure S2) and rounded to the nearest 100, following the approach of Hensen et al. (2005).
A "population" was defined as a group of individuals of the same species separated by their closest conspecifics by at least 100 m or by natural barriers such as agricultural areas or forests (Bachmann & Hensen, 2006). We collected 20 T. montanum individuals for functional trait and population genetic analyses at each location (sampling points were distributed equally within a habitat). In total, we sampled up to 20 individuals per population and 260 individuals in TA B L E 1 Sampling localities of Trifolium montanum populations in Central Germany and Austria (KW; see also Karbstein, Prinz, et al., 2020 F I G U R E 1 (a) European distribution range of Trifolium montanum (mountain clover) highlighted in gray according to Meusel and Jäger (1998). The black square indicates the sampling area in Germany. The black dot shows the sampling location in Austria ("KW").
(b) Sampling localities in Central Germany (see Table 1 for abbreviations and further information

| Assessment of habitat characteristics, population genetics, and functional traits
As described in Karbstein, Prinz, et al. (2020), data preprocessing and filtering of environmental factors and functional traits were done with R in order to remove outliers or collinearities (see also Dormann et al., 2013), and manual editing of microsatellite marker raw data was performed to remove ambiguous scoring results. In this study, we additionally evaluate comprehensive biotic vegetation record data, habitat area estimates, and population sizes (additionally classified as "small" and "large" at the median of the distribution; small = 50 to 1100 individuals, and large = 2300 to 20,900 individuals) together with the previously analyzed environmental, genetic, and functional trait variables.
To characterize the environmental conditions per location (  et al., 2001). All environmental factors are listed in Table 2. We also used the calculated abiotic within-habitat heterogeneity (HD) as mean coefficient of variation (CV, ratio of the standard deviation to the mean) based on nonautocorrelated environmental factors from Karbstein, Prinz, et al. (2020): altitude (CV altitude ), slope exposure (CV slope exposure ), slope (CV slope ), leaf area index (CV LAI ), soil depth (CV soil depth ), soil cation-exchange capacity (CV CECpot ), pH (CV pH ), soil nitrogen content (CV N ), soil phosphor content (CV P ), and soil potassium content (CV K ; Table 2; see also Tables 1 and 2 et al., 2000) analyses in Karbstein, Prinz, et al. (2020). In addition, we performed a PCoAs based on Nei's genetic distances to ensure that sampled individuals within populations did not represent clones that would bias genetic diversity and differentiation indices ( Figure S6).
We then assessed the following functional traits based on 260 individuals in 13 populations: Releasing height (RH), total dry aboveground biomass (AGB), leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), the ratio of variable fluorescence to maximum fluorescence (F v /F m ), performance index on absorption basis (PI), stomatal pore surface (SPS), and stomatal pore area index (SPI; Balasooriya et al., 2009;Cornelissen et al., 2003;Pérez-Harguindeguy et al., 2013;Sack et al., 2003;Strasser et al., 2000;Strasser et al., 2004). By sampling only flowering to early fruiting individuals, we ensured the comparability of functional traits among the populations studied (see also Römermann et al., 2016). All functional traits are listed in Table 4. Finally, we used the mean CV of all traits as intraspecific functional trait variation (iFD CV ). This measure focuses on trait variation rather than trait differences, captures trait space and variation, and is suitable for studying environmental and genetic effects on trait variation (Karbstein, Prinz, et al., 2020).  Karbstein, Prinz, et al., 2020).   (wK), soil humidity (wF), temperature (wT), soil acidity (wR), and soil fertility (wN), and cover percentages of herb layers and bare ground. *=Abiotic environmental factor used to calculate habitat heterogeneity (HD) in Karbstein, Prinz, et al. (2020). See cited Dryad data repository in Section (Data Availability Statement) for environmental raw data. Statistics were performed to infer location-dependent differences among environmental factors (***p < .001, **p < .01, see Section 2.4).

| Statistical data analyses
Abbreviations: CEC pot , potential cation-exchange capacity; E, evenness; exp., slope exposure; K, soil potassium content; N, soil nitrogen content; P, soil phosphor content; P a , annual precipitation; S, species richness; T a , annual temperature.
on environmental factors only (axis lengths < 1). Second, we ran a PCA based on functional traits (axis lengths < 1), and correlated the PCA axes with environmental factors (1000 permutations) and only showed the significant factors (p < .05). Populations were highlighted according to size to examine their environmental and trait (dis)similarity. In addition, we correlated each PCA axis with population TA B L E 3 Mean genetic properties of 13 Trifolium montanum populations in Central Europe using nine microsatellite markers (see also Karbstein, Prinz, et al., 2020). Abbreviations of locations are explained in Table 1.   Karbstein, Prinz, et al., 2020). In addition, spatial autocorrelation among populations/habitats with respect to HD, GD, and iFD CV was checked before using Moran's I values (Moran, 1950), and found to be very weak (Karbstein, Prinz, et al., 2020). Therefore, we did not run specialized LMs accounting for spatial autocorrelation. For multiple linear regression models, we standardized explanatory variables to zero mean and unit variance. Simplification was conducted using the backward selection method: we always excluded the least significant variable (p > .1) until we reached the final model (Crawley, 2015). Then, we carried out ANOVAs and additionally calculated the Akaike information criterion (AIC) to control each simplification step. We checked the final models for normality, homoscedasticity, and linearity.  Karbstein, Prinz, et al., 2020).   Table 1 See the cited Dryad data repository in Section (Data Availability Statement) for trait raw data. Statistics were performed to infer location-wise differences among functional traits (***p < .001; see Section 2.4).

| Environment, traits, and population size
Central German locations are spread across different landscapes, representing an environmental gradient from relatively warm and dry lowlands (Thuringian Basin) to cooler and wetter Central German mountain systems (Thuringian Forest, Rhoen Mountains; Table 2).
The PCA based on abiotic and biotic environmental factors explained 45% of variation with the first two principal components Germany. Riezlern (KW) was removed from the PCA due to its substantially different climatic conditions compared to the Central German locations, and the ordinal variable slope exposure was excluded from the analysis. The first two principal components explained 45% of variation. PCA axis 2 with correlated factors in bold (wK, wL, P a , species richness, T a , altitude) is mainly and significantly responsible for the separation of populations according to size (see Section 3.1). (b) PCA of mean functional traits including 12 T. montanum populations in Central Germany. Riezlern (KW) was excluded due to missing abiotic and biotic environmental factors (F v /F m , PI, and crop height). Here, no PCA axis correlated significantly with population size. Environmental factors significantly correlated with PCA axes are indicated by red arrows. The first two principal components explained 68% of trait variation. The size of location circles represents population size ( Table 1). Classification of population size was done at the median of the distribution. Eh; Figure 2a) population sizes are smallest, which is associated with high nutrient availability and biotic competition (high wN, wF, and LAI, low wR). Moreover, all abiotic and biotic environmental factors differed significantly among locations (p < .05; Table 2). Variation in population size (50 to 20,900 individuals; Table 1) is not correlated to abiotic habitat heterogeneity (HD; R 2 = .09, p = .32), and HD did not significantly depend on habitat area (R 2 = .04, p = .49).
In contrast to multivariate PCA and related correlation results, a multiple LM between population size and single environmental factors exhibited no significant relationships (R 2 adj = .00, F 2,10 = 0.33, p = .90), even after model simplification. Single LMs for each environmental factor separately also revealed no significant associations  Table 4). LM results also showed that functional traits were significantly affected by their abiotic environment and that the direction of correlation was highly trait-dependent and complex across traits (Table S2).  (Table 3). LMs revealed significant positive mean relationships between population size and genetic diversity indices N A (R 2 = .42, p < .05), H o (R 2 = .67, p < .001), H e (R 2 = .43, p < .05), and I (R 2 = .59, p < .01; Figure 3a,c-e). F IS is significantly negatively related to population size (R 2 = .35%, p < .05; Figure 3f). No significant linear relationships were found between population size and P Ap (R 2 = .10, p = .29; Figure 3b) and G ST (R 2 = .20%, p = .13; Figure 3g). Population size and G ST are significantly negatively associated (R 2 = .38%, p < .05) when the population Steinthaleben (St) is removed (Figure 3h).

| Population genetic indices and population size
Regarding the relationships between population genetic indices, the strongest significant positive relationships were found between Tm21, which showed a three-fold larger size range (Table S2). The total N A was 146, including all nine microsatellite loci.

| Path analysis to model iFD CV (ITV)
The path analysis (local SEM) represents the formulated hypothesis and underlying data well (Fisher's C = 146.50,p = .52 ; Table S3).
In detail, we observed significant relationships between population size and CV LAI (23%, p < .01), CV soil depth (22%, p < .01), CV K (21%, p < .01), CV N (12%, p < .05), CV pH (12%, p < .05), and CV altitude (10%, p < .05). In contrast to previous LM analyses, CV P and CV slope exposure were not correlated with population size. With increasing variation in soil depth, soil nitrogen content, and altitudes, and decreasing variation of light availability, soil potassium content, and soil pH within In particular, our study made progress by comprehensively reconstructing relevant processes that influence ITV and population performance, and unraveling population size as the most critical F I G U R E 3 Relationships between population genetic indices (a) allelic richness, (b) private allelic richness, (c) expected heterozygosity, (d) observed heterozygosity, (e) Shannon's diversity index, (f) inbreeding coefficient, (g) genetic differentiation, and (h) genetic differentiation without population St and population size of 13 Trifolium montanum populations in Central Europe based on nine microsatellite markers and 255 individuals (see Tables 1 and 4). Linear regression models were performed with log-transformed population sizes, and ln-functions were fitted to the untransformed data set. Curves of nonsignificant relationships (p > .05) were not drawn.
factor. In contrast to expectations, population size was not linearly affected by abiotic environmental factors (see also center vs. niche distribution, Figure 2a) but was almost completely explained by the variation of certain abiotic environmental habitat factors. With rising population size, genetic diversity (H e , N A , H o , I) increased, whereas inbreeding (F IS ) and genetic differentiation (G ST ) decreased in T. montanum ( Figure 3). Finally, ITV (iFD CV ) could be largely attributed to habitat heterogeneity (68%) and to a lesser extent to genetic diversity (H e , 32%; Figure 4). Population size via population genetic consequences, therefore, represents an important, but interestingly not the most important factor shaping ITV in T. montanum populations. The here investigated positive relationships among population size, genetic diversity, and ITV as an indicator for performance (e.g., Hensen et al., 2005;Leimu et al., 2006;Reisch et al., 2021;Rosche et al., 2022), and among genetic diversity, habitat heterogeneity, and ITV (e.g., Karbstein, Prinz, et al., 2020;Waitt & Levin, 1998) are consistent with literature. In general, small as opposed to large T. montanum populations are characterized by medium to extreme environmental habitat factor and functional trait values (several niche pessima), higher (LAI, soil K and pH) and lower (soil depth and N, altitude) variability of certain abiotic environmental factors, lowered genetic diversity, elevated inbreeding and differentiation, and finally lower ITV and performance (Table 5).  Table 5). This observation is consistent with ACH predictions and observations for T. montanum that optimal habitat quality (niche optimum) leads to high growth rates and population sizes, whereas nonoptimal habitat quality (niche pessimum) results in low growth rates and small population sizes, which are characterized by nontypical or novel phenotypic trait responses (Brown, 1984;Hirsch et al., 2015;Leimu et al., 2006;Schleuning et al., 2009;Schleuning & Matthies, 2008). Large T. montanum populations were more abundant in extensively used, species-rich Bromus erectus (Mesobromion) habitats with relatively high light availability, moderate continentality, temperatures, and precipitation, and low biotic competition, located along shrub and forest edges as well as way-and roadsides.

| Environment, traits, and population size
According to the literature, T. montanum should occur predominantly in nutrient-poor, calcareous, sub-Mediterranean to continental grasslands (Jäger, 2011;Schleuning et al., 2009), but we have also found populations of various sizes on weak acidic to pH-neutral (e.g.,

F I G U R E 4 A framework of inferred relationships in
Trifolium montanum among intraspecific trait variation (iFD CV ), abiotic within-habitat heterogeneity (HD), population genetic diversity (N A , P Ap , H e = GD, H o , I), inbreeding (F IS ), and differentiation indices (G ST ) shaped by population size, which is in turn affected by variation of abiotic environmental habitat factors. Results are based on the SEM analysis (local SEM; see Section 3.3 for details). Significant relationships are indicated with solid black arrows, whereas nonsignificant relationships are shown as dashed lines. Black solid lines indicate dependencies due to mathematical calculations, and gray lines assumed causal relationships from examined literature (link between genetic diversity inbreeding/trait variation to plant population performance and fitness). The color scheme was taken from previous Figures 2 and 3, and the basic concept from Karbstein, Prinz, et al., 2020 (published under Creative Commons License, redrawn herein). N A = allelic richness (total number of alleles), H e = expected heterozygosity, H o = observed heterozygosity, F IS = inbreeding coefficient, I = Shannon's diversity index, G ST = differentiation of a given population relative to all populations.
in Bottendorf Bo, Bad Frankenhausen Ba), moderately nutrient-rich soils (e.g., in Niederwillingen Ni, Steinthaleben St), suggesting tolerance to different pH and nutrient conditions in Central Germany.

Although T. montanum's range center is situated in Eastern
Europe characterized by rather continental climate, the species prefers moderate Mesobromion meadows in Central Germany and did not cope well with continental steppe grasslands (e.g., Bo, Ba).

| Environment, population size, genetics, and ITV
Population size is almost entirely explained by abiotic environmental variation within habitats. Although many studies have examined relationships among plant population size, genetic diversity, and/or performance or fitness (e.g., De Kort et al., 2021;Leimu et al., 2006;Rosche et al., 2022;Szczecińska et al., 2016), they have focused less on how population growth rate and size depend on environmental habitat factors and/or variation within these statistical frameworks (Lawson et al., 2015;Nicolè et al., 2011;Schleuning et al., 2009;Schleuning & Matthies, 2008). Plants are sessile organisms, and thus particularly susceptible and vulnerable to spatiotemporal environmental variation (Karbstein et al., 2019;Nicolè et al., 2011).
Environmental factors thus likely influence population growth rates and size in T. montanum (e.g., as shown for light intensity and biotic competition in Schleuning et al., 2009;Schleuning & Matthies, 2008), but T. montanum is a less competitive semi-dry grassland species, requiring extensive grassland land-use management to ensure long-term viability of populations. For example, large T. montanum populations are characterized by increased LAI, soil pH, K, and slope ( Figure 4). Increased variation in these factors indicates habitats with patches of high and low light, specific nutrients, and biotic competition conditions that reduce the dominance of grass species and allow the presence of less competitive species like T. montanum. In contrast, reduced variation in slope exposure, slope, and soil N leads to large population sizes because T. montanum prefers north-exposed, flat, rather nutrient-poor habitats. Spatial variation in environmental factors thus overrides the effects of mean environmental factors, a phenomenon that has rarely been studied in detail in plant populations (temporal variation reviewed, e.g., in Lawson et al., 2015).
In T. montanum, population size strongly determines genetic diversity (N A , H o , H e , I) and inbreeding (F IS ), and partly differentiation TA B L E 5 Characterization of large and small T. montanum populations in terms of environment, genetics, and traits and according to the results of this study (Figures 2-4, Tables 1-4, see Section 2.3 for population classification). (G ST , Figures 3 and 4). Large T. montanum populations such as ST or Er with more than 10,000 individuals show increased genetic diversity and decreased inbreeding, high ITV, and good performance (as directly indicated by PI and F v /F m ; Table 4). These large populations probably contain many different, heterozygous genotypes due to increased gene flow (efficient pollinator activity in large populations) and genetic recombination, and thus less inbreeding and genetic drift effects. In contrast, very small T. montanum populations such as Di surrounded by agrarian areas ( Figure S2) are likely to suffer under restricted gene flow within but also with surrounding populations, and thus perpetuating and amplifying genetic drift, inbreeding, and decreasing performance (as directly indicated by PI and F v / F m ; Table 4) may result in extinction (Freeland et al., 2011;Leimu et al., 2006;Rosche et al., 2022;Schleuning et al., 2009).
In general, associations between population genetic indices and population size are strong at low and weak or absent ("saturated") at larger sizes (reviewed in Leimu et al., 2006;e.g., Luijten et al., 2000;Rosche et al., 2022). This general pattern was also confirmed here, except for private allelic richness probably due to the geographically narrow sampling (Kalinowski, 2004; Figure 3). In contrast to Leimu et al. (2006), in small T. montanum populations, the loss of allelic richness and thus genetic drift was less important compared to the loss of observed heterozygosity and thus homozygosity and inbreeding.
The long-lived nature of mountain clover ( Figure S1) might explain this observation, as perennials compared to annuals are less vulnerable to pollinator limitation or demographic stochasticity in recruitment, and genetic drift (Freeland et al., 2011;Hamrick et al., 1979;Leimu et al., 2006).
Interestingly, Leimu et al. (2006) investigated no general relationship between inbreeding and population size across species due to equivocal results between self-compatible and selfincompatible species. In T. montanum, reduced genetic diversity at self-incompatibility loci probably leads to a decreased number of potential mating partners in populations and a decrease in female fitness (Fischer et al., 2003;Karbstein, Rahmsdorf, et al., 2020;Willi et al., 2005), which is supported by observation of strong decrease in reproduction with dropping local individual density and pollinator activity (Schleuning et al., 2009). Consequently, with decreasing size, homozygotes meet more frequently, accelerating the vortex of inbreeding and extinction. A breakdown in self-incompatibility may alter these relationships (e.g., Porcher & Lande, 2005;Trifolium: Frye & Neel, 2017), but this has not yet been observed in T. montanum.
Self-incompatible species, as already mentioned, but also neutral DNA markers like microsatellites favor a strong relationship between population size and genetic diversity (Leimu et al., 2006).
Natural selection is less acting on neutral markers leading to higher genetic variability and thus increased potential in explaining relationships to population size (Frankham, 1996;Leimu et al., 2006;Rosche et al., 2022). Genetic differentiation is also elevated in smaller T. montanum populations. This is probably caused by less gene flow with surrounding populations due to habitat fragmentation (e.g., small population Dielsdorf surrounded by agrarian area, and Ehrenberg surrounded by forests, Figure S2) and density-dependent pollinator activity within populations, leading to higher genetic isolation of smaller populations.
Genetic diversity (H e ) represents a critical prerequisite for high variability of functional traits within populations, ITV (iFD CV ), and therefore population performance (Figure 4). Nevertheless, ITV in T. montanum populations is mainly generated by the response of genotypes to abiotic environmental habitat heterogeneity (HD). The interaction between H e and HD did not affect ITV, suggesting phenotypic plasticity-based ITV, rather than ITV associated with specific site-adapted genotypes.
Accordingly, although abiotic environment can act on genetic diversity via natural selection (Linhardt & Grant, 1996;Reisch et al., 2021;Sakaguchi et al., 2019), we did not detect selective pressure here, likely due to the applied neutral marker type and to insufficient abiotic selective pressures within habitats (Figure 4)

| Long-term viability and nature conservation of Trifolium montanum populations in semi-dry grasslands
This research improves the theoretical understanding of relationships among population size, environment, genetic diversity, and inbreeding, and ITV as an indicator of plant performance. It has several implications for applied biodiversity and nature conservation. Trifolium montanum populations in nonoptimal habitats are characterized by reduced genetic and intraspecific functional trait diversity, and increased genetic inbreeding and differentiation. These signals indicate a decreased plant performance and fitness, and therefore, reduced adaptability to current and future environmental changes, and elevated extinction risk (Ellstrand & Elam, 1993;Karbstein, Rahmsdorf, et al., 2020;Leimu et al., 2006;Spielman et al., 2004). The fate and long-term survival of small populations will be highly dependent on adequate habitat protection and land-use actions to stabilize population sizes and escape the vortex of extinction (Ellstrand & Elam, 1993;Leimu et al., 2006;Rosche et al., 2022). For example, habitat degradation and fragmentation are well-known to reduce population size and density, increase isolation, and limit gene flow, all of which negatively affect genetic diversity and ITV Hensen & Wesche, 2006;González et al., 2020;Karbstein, Prinz, et al., 2020).
In order to stabilize or rescue small T. montanum populations, it is important to first improve habitat quality according to environmental preferences (niche optimum) to ensure sufficiently high population growth rates, and second, to increase the habitat area of a given population, either by enlarging suitable habitat area or by connecting previously isolated habitats. Applied to T. montanum populations in Central Germany, optimal habitats are characterized by extensively managed, species-rich, calcareous Bromus erectus semi-dry grasslands with low vegetation density (less biotic grass competition), and moderate soil nutrient supply and humidity (Figure 2a).
For T. montanum, studies have shown that the consequences of habitat degradation are more important than those of habitat fragmentation in the short term. In unmanaged sites, population growth rates decrease with increasing light competition (LAI) because of higher investment in plant height and lower investment in flowering structures, recruitment, and survival, resulting in aged populations (Schleuning et al., 2009;Schleuning & Matthies, 2008). Extinction in these perennials is likely to take a long time, and even very small populations can persist for decades until extinction (e.g., up to ca. 30 years old individual observed in this study, Figure S1b). Currently, abandonment of land use and habitat eutrophication due to nitrogen deposition are most problematic for open, oligotrophic grasslands, allowing for the dominance of certain grasses while reducing less competitive species (Habel et al., 2013) such as T. montanum. Appropriate land-use management (e.g., frequent animal grazing, or occasional mowing to prevent succession) can rapidly increase population growth rates of even small T. montanum populations and reduce the risk of population extinction (Schleuning et al., 2009). Small populations revealed relatively low but still moderate genetic diversity and signs of inbreeding, suggesting that populations may have a temporally limited potential to persist in these nonoptimal habitats. Interestingly, individuals from these small T. montanum populations were often not highly stressed (Figure 2b, Tables 2-4). Good nutrient and water supply, and moderate biotic competition and inbreeding may explain this observation. Nevertheless, in general, adequate nature conservation actions need to be taken in the near future to ensure the long-term survival of T. montanum populations.

ACK N OWLED G M ENTS
The Institute of Ecology and Evolution (Friedrich Schiller University, Jena, Germany) financially supported this research.
We thank the lower nature conservation authorities ("UNB")

CO N FLI C T O F I NTE R E S T S TATE M E NT
None.

DATA AVA I L A B I L I T Y S TAT E M E N T
Basic data supporting the findings of this study are available within the manuscript and the Appendix S1. Environmental, genetic, and functional trait data are available on Dryad data repository (https:// doi.org/10.5061/dryad.n02v6 wwtd). Functional trait data are additionally deposited on TRY database (www.try-db.org).

CO D E AVA I L A B I LIT Y
R scripts used in analyses are available from the corresponding author on request.